1 Introduction

MicroRNAs (miRNAs) are small non-coding RNAs (~22 nt) that control a
wide range of biological processes including cancers via regulating
target genes [1-5]. Therefore, it is important to uncover miRNA functions
and regulatory mechanisms in cancers.

The emergence of competing endogenous RNA (ceRNA) hypothesis [6]
has challenged the traditional knowledge that coding RNAs only act
as targets of miRNAs. Actually, a pool of coding and non-coding
RNAs that shares common miRNA biding sites competes with each other,
thus act as ceRNAs to release coding RNAs from miRNAs control.
These ceRNAs are also known as miRNA sponges or miRNA decoys, and
include long non-coding RNAs (lncRNAs), pseudogenes, circular RNAs
(circRNAs) and messenger RNAs (mRNAs), etc [7-10]. Recent
studies [11, 12] have shown that miRNA sponge network and module
can help to reveal the biological mechanism in cancer.

To accelerate the research of miRNA sponge, we develop an R package
‘miRsponge’ to implement popular methods in the identification and
analysis of miRNA sponge network and module.

2 Identification of miRNA sponge interactions

In ‘spongeMethod’ function, We implement seven popular methods
(miRHomology [13, 14], pc [15, 16], sppc [17], ppc [11], hermes [18],
muTaME [19], and cernia [20]) to identify miRNA sponge interactions.
The seven methods should meet a basic condition: the significance of
sharing of miRNAs by each RNA-RNA pair (e.g. adjusted p-value < 0.01).
Each method has its own merit due to different evaluating indicators.
Thus, we present an integrate method to combine predicted miRNA
sponge interactions from different methods.

2.1 miRHomology

We implement miRHomology method based on the
homology of sharing miRNAs.

Parameter ‘num_perm’ is used to set the number of permutations of input
expression data. The larger the number is, the slower the calculation is.

2.6 muTaME

We implement the muTaME method based on the logarithm
of four scores: (1) the fraction of common miRNAs, (2) the density of the
MREs for all shared miRNAs, (3) the distribution of MREs of the putative
RNA-RNA pairs and (4) the relation between the overall number of MREs for
a putative miRNA sponge compared with the number of miRNAs that yield these
MREs. There is no reason to decide which score has more contribution than
the rest. Thus, we calculate a combined score by adding these four scores.
To evaluate the strength of each RNA-RNA pair, we further normalize the combined
scores to obtain normalized scores with interval [0 1].

2.7 cernia

We implement the cernia method based on the
logarithm of seven scores: (1) the fraction of common miRNAs, (2)
the density of the MREs for all shared miRNAs, (3) the distribution
of MREs of the putative RNA-RNA pairs, (4) the relation between the
overall number of MREs for a putative miRNA sponge compared with the
number of miRNAs that yield these MREs, (5) the density of the
hybridization energies related to MREs for all shared miRNAs, (6)
the DT-Hybrid recommendation scores and (7) the pairwise Peason
correlation between putative RNA-RNA pair expression data. There
is no reason to decide which score has more contribution than the
rest. Thus, we calculate a combined score by adding these seven scores.
To evaluate the strength of each RNA-RNA pair, we further normalize
the combined scores to obtain normalized scores with interval [0 1].

Parameter ‘Intersect_num’ is used to set the least number of methods
intersected for integration. That is to say, we only reserve those
miRNA sponge interactions predicted by at least ‘Intersect_num’ methods.

3 Validation of miRNA sponge interactions

To validate the predicted miRNA sponge interactions, we implement
‘spongeValidate’ function. The groundtruth of miRNA sponge interactions
are from miRSponge (http://www.bio-bigdata.net/miRSponge/) and the experimentally validated miRNA sponge interactions of related literatures.

Parameter ‘devidePercentage’ is used to set the percentage of high risk group.

7 Conclusions

miRsponge provides several functions to study miRNA sponge (also called ceRNA or miRNA decoy), including popular methods for identifying miRNA sponge interactions, and the integrative method to integrate miRNA sponge interactions from different methods, as well as the functions to validate miRNA sponge interactions, and infer miRNA sponge modules, conduct enrichment analysis of modules, and conduct survival analysis of modules. It could provide a useful tool for the research of miRNA sponges.